Orientation predicting method, virtual reality headset and non-transitory computer-readable medium

ABSTRACT

An orientation predicting method, adapted to a virtual reality headset, comprises obtaining an orientation training data and an adjusted orientation data, wherein the adjusted orientation data is obtained by cutting a data segment off from the orientation training data, wherein the data segment corresponds to a time interval determined by an application latency; training an initial neural network model based on the orientation training data and the adjusted orientation data corresponding to the time interval; retrieving a real-time orientation data by an orientation sensor of the virtual reality headset; and inputting the real-time orientation data to the trained neural network model to output a predicted orientation data. The present disclosure further discloses a virtual reality headset and a non-transitory computer-readable medium.

BACKGROUND 1. Technical Field

This disclosure relates to an orientation predicting method, a virtualreality headset and a non-transitory computer-readable medium, inparticular, relates to an orientation predicting method, a virtualreality headset and a non-transitory computer-readable medium forpredicting a head motion of a user using a virtual reality headset.

2. Related Art

The technology of virtual reality (VR) with a head mounted display (HDM)has evolved rapidly. The VR technology has already been applied invarious fields, from entertainments such as video games, navigation,virtual traveling, education, even to medical field where surgeonspracticing or performing surgery with HDM devices.

VR technology uses artificial sensory simulation to induce the userperforming a targeted behavior with the user having minimum awarenessabout the interference. However, the artificial sensory simulation mayfail to accurately create a perceptual illusion for the user due tomotion-to-photon (MTP) latency. That is, a latency may occur between thedisplayed image and the user's motion due to the fact that there existsa time interval between the user's motion and the resulting update of anew frame on the HDM device. And motion-to-photon latency may cause theuser to have motion sickness.

In order to solve the above-mentioned problem, head movement predictionis the main solution to compensate the latency. That is, theconventional method for predicting head movement uses extrapolation andfilter-based prediction method based on two or more sets of previousdata to reduce noise and predict the user's head movement, so as toreduce or compensate for the latency.

SUMMARY

Accordingly, this disclosure provides an orientation predicting method,a virtual reality headset and a non-transitory computer-readable mediumto solve the above-mentioned problems and to provide a better userexperience when using the virtual reality headset.

According to one or more embodiment of this disclosure, an orientationpredicting method, adapted to a virtual reality headset, comprising:obtaining an orientation training data and an adjusted orientation data,wherein the adjusted orientation data is obtained by cutting a datasegment off from the orientation training data, wherein the data segmentcorresponds to a time interval determined by an application latency;training an initial neural network model based on the orientationtraining data and the adjusted orientation data for obtaining a trainedneural network model corresponding to the time interval; retrieving areal-time orientation data by an orientation sensor of the virtualreality headset; and inputting the real-time orientation data to thetrained neural network model to output a predicted orientation data.

According to one or more embodiment of this disclosure, a virtualreality headset, comprising: an orientation sensor, retrieving thereal-time orientation data of the virtual reality headset; a processor,inputting the real-time orientation data to a trained neural networkmodel of the processor for obtaining a predicted orientation data,wherein the trained neural network model is obtained by training aninitial neural network model based on an orientation training data andan adjusted orientation data, wherein the adjusted orientation data isobtained by cutting a data segment off from the orientation trainingdata, wherein the data segment corresponds to a time interval determinedby an application latency; and a screen, displaying a predicted imageaccording to the predicted orientation data.

According to one or more embodiment of this disclosure, a non-transitorycomputer-readable medium, storing an executable instruction which, whenexecuted, causes a virtual reality headset to perform a methodcomprising: retrieving a real-time orientation data by an orientationsensor and inputting the real-time orientation data to a trained neuralnetwork model to output a predicted orientation data, wherein thetrained neural network model is obtained by training an initial neuralnetwork model based on an orientation training data and an adjustedorientation data, wherein the adjusted orientation data is obtained bycutting a data segment off from the orientation training data, whereinthe data segment corresponds to a time interval determined by anapplication latency.

In view of the above description, according to one or more embodimentsof the orientation predicting method, a virtual reality headset and anon-transitory computer-readable medium of the present disclosure, theMTP latency can be effectively reduced and an accurate head movement ofthe user can be made, therefore, the user can have a better experiencewhen using the virtual reality headset without having motion sickness.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description given hereinbelow and the accompanying drawingswhich are given by way of illustration only and thus are not limitativeof the present disclosure and wherein:

FIG. 1 is a block diagram illustrating a virtual reality headsetaccording to an embodiment of the present disclosure;

FIG. 2 is a flowchart illustrating an orientation predicting methodaccording to an embodiment of the present disclosure;

FIG. 3 is a flowchart illustrating an orientation predicting methodaccording to another embodiment of the present disclosure;

FIGS. 4A and 4B are statistic charts showing differences between thepredicted orientation data and the real-time orientation data based on a50 ms latency respectively obtained from using the extrapolation methodand using the orientation predicting method of the present disclosure;and

FIGS. 5A and 5B are statistic charts showing differences between thepredicted orientation data and the real-time orientation data based on a100 ms latency respectively obtained from using the extrapolation methodand using the orientation predicting method of the present disclosure.

DETAILED DESCRIPTION

Please refer to both FIGS. 1 and 2, wherein FIG. 1 is a block diagramillustrating a virtual reality headset according to an embodiment of thepresent disclosure; and FIG. 2 is a flowchart illustrating anorientation predicting method according to an embodiment of the presentdisclosure.

The virtual reality headset 1 of the present disclosure comprises aprocessor 10, an orientation sensor 11, a memory 12 and a screen 13,wherein the virtual reality headset 1 may be a head mounted display(HDM) device. Specifically, with the data retrieved by the orientationsensor 11 and a neural network model stored in the memory 12, theprocessor 10 can activate the screen 13 to display images, and thevirtual reality headset 1 can perform the orientation predicting method.That is, when an executable instruction stored in a non-transitorycomputer-readable medium is executed by the processor 10, the virtualreality headset 1 can be instructed to perform the orientationpredicting method.

Further, the orientation sensor 11 is configured to retrieve thereal-time orientation data of the virtual reality headset 1. Theorientation sensor 11 is preferably an inertial measurement unit (IMU)sensor comprising a triaxial accelerometer, a gyroscope and amagnetometer, however, the present disclosure is not limited thereto.The orientation sensor 11 can also be any other type of orientationsensor that can detect pitch, roll, and yaw movements of the user(virtual reality headset 1).

The memory 12 of the virtual reality headset 1 can store the dataretrieved by the orientation sensor 11 as well as one or more neuralnetwork models that are used for predicting a user's head movement. Thescreen 13 of the virtual reality headset 1 can display imagescorresponding to the predicted head movement of the users.

It should first be noted that, steps S10 and S20 are preferablyperformed by a computer or any other computing device before step S30.In other words, steps S10-20 are steps for establishing a trained neuralnetwork model which is preferably established before receiving thereal-time orientation data from the orientation sensor 11. The steps ofestablishing the trained neural network model are preferably performedby a computing device of the manufacturer, or by first accumulating aplurality of orientation training data and then establishing the trainedneural network model by a computing device of the end user. The steps ofestablishing the trained neural network model can also be performed bythe processor 10 of the virtual reality headset 1, the presentdisclosure is not limited thereto. The following steps of establishingthe trained neural network model will be illustrated being performed bythe processor 10.

Please refer to FIG. 2 again, step 10: Obtaining an orientation trainingdata and an adjusted orientation data.

The processor 10 obtains an orientation training data and an adjustedorientation data for the training of an artificial intelligence (AI)model, which is preferably a neural network model, wherein the adjustedorientation data is obtained by cutting a data segment off from theorientation training data, and the data segment corresponds to a timeinterval determined by an application latency. The application latencyis, for example, a motion-to-photon (MTP) latency.

Specifically, both the orientation training data and the adjustedorientation data are previously obtained data and preferably comprisepitch, roll, and yaw data. The difference between the orientationtraining data and the adjusted orientation data lies in that, theadjusted orientation data is obtained by cutting the data segment offfrom the orientation training data, wherein the data segment relates tothe time interval (application latency) which is determined according tothe application run by the virtual reality headset 1 when theorientation sensor 11 is obtaining the orientation training data.

For example, there may be a 50 ms application latency between theorientation training data and the adjusted orientation data, and the 50ms application latency can be used as the time interval. The latency maybe caused by tracking delay, networking delay, application delay,rendering delay and/or display delay of the virtual reality headset 1.Therefore, the adjusted orientation data can be obtained by cutting thedata segment corresponding to the 50 ms application latency from theorientation training data. In other words, the adjusted orientation datais the orientation training data that is 50 ms later.

Step 20: training an initial neural network model based on theorientation training data and the adjusted orientation data.

The processor 10 can train the initial neural network model based on theorientation training data and the adjusted orientation data forobtaining a trained neural network model corresponding to the timeinterval, wherein the initial neural network model preferably comprisesa one-dimensional convolutional neural network (1D-CNN). Specifically,since each of the orientation training data and the adjusted orientationdata includes data in yaw, pitch and roll dimensions independent of eachother, and the data representing any one of the three dimensions ispreferably the angles of yaw/pitch/roll at time points, the 1D-CNN isextremely suitable to serve as the initial neural network model.However, the neural network model may also comprise a fully connectednetwork (FCN), a long-short term memory (LSTM) and a convolutionalneural network (CNN). The orientation training data and the adjustedorientation data are inputted to the initial neural network model totrain the model to determine a predicted orientation data when receivinga real-time orientation data.

It should be noted that, the neural network model can also be a hybridof the ID-CNN and the FCN or the CNN, wherein the models mentionedherein are examples and not to limit the present disclosure while theneural network model can be chosen based on the input and output datatype.

In practice, before start using the virtual reality headset 1, the usercan be asked to perform some head movements to collect the orientationtraining data and the adjusted orientation data for training the initialneural network model. With this approach, the trained neural networkmodel can predict a head movement that fits more to the user's movinghabits (such as speed or angle) and the corresponding application run bythe virtual reality headset 1.

Step 30: retrieving a real-time orientation data.

That is, after the initial neural network model is trained and thetrained neural network model is obtained, the orientation sensor 11 canretrieve the real-time orientation of the virtual reality headset 1. Thereal-time orientation is preferably the same data type of theorientation training data and the adjusted orientation data. Therefore,in the present embodiment, the real-time orientation preferablycomprises pitch, roll, and yaw data.

Step S40: inputting the real-time orientation data to the trained neuralnetwork model to output a predicted orientation data.

That is, after obtaining the trained neural network model, the processor10 can receive the real-time orientation data from the orientationsensor 11 and input the real-time orientation data to the trained neuralnetwork model. Therefore, the processor 10 can output the predictedorientation data, wherein the predicted orientation data represents thefuture head movement of the user (virtual reality headset 1).

In other words, take the above-mentioned 50 ms application latency as anexample, since the virtual reality headset 1 has the 50 ms applicationlatency between the user's real-time head movement and the imagedisplayed by the screen 13 of the virtual reality headset, the processor10 can input the real-time orientation data into the trained neuralnetwork model that is trained with the 50 ms application latency data(the orientation training data and the adjusted orientation data). Theprocessor 10 then can obtain the predicted orientation data output fromthe trained neural network model. Accordingly, the application latencycan be reduced and the screen 13 can display a predicted image thatcorresponds to the predicted orientation data.

Please refer to FIGS. 1 and 3, wherein FIG. 3 is a flowchartillustrating an orientation predicting method according to anotherembodiment of the present disclosure. That is, the orientationpredicting method illustrated in FIG. 3 is similar to that of FIG. 2,the difference between FIGS. 2 and 3 is that, after obtaining thetrained neural network model (step S20), and before retrieving thereal-time orientation data (step S30), the orientation predicting methodillustrated in FIG. 3 further comprises steps S21 and S22. Specifically,steps S10 and S20 can be performed multiple times for different timeintervals, so as to obtain a plurality of candidate neural networkmodels. And the obtained candidate neural network models can be storedin the memory 12.

In other words, after obtaining the trained neural network model in stepS20, the processor 10 can perform step S21: estimating a latency of anapplication.

That is, the virtual reality headset 1 runs an application when inoperation, wherein the application may be a video game or other types ofvirtual reality applications. The processor 10 of the virtual realityheadset 1 can estimate the latency of the application for determiningwhich trained neural network model to use for predicting the predictedorientation data.

Step S22: selecting the trained neural network model from a plurality ofcandidate neural network models according to the time delay.

As mentioned above, the memory 12 may store a plurality of candidateneural network models, wherein the candidate neural network modelscorrespond to different time intervals respectively.

Therefore, the processor 10 estimates the latency of the application todetermine which candidate neural network model to use to obtain thepredicted orientation data. To be more specific, the processor 10 cantrain a plurality of initial neural network models in advance withdifferent time intervals to obtain the plurality of candidate neuralnetwork models. For example, the plurality of candidate neural networkmodels may be obtained from training a plurality of initial neuralnetwork models with a 15 ms, a 30 ms, a 50 ms, and a 100 ms timeinterval respectively. The mentioned time intervals are merely examples,the present disclosure is not limited thereto.

Therefore, after estimating the latency of the application that is runby the virtual reality headset 1, the processor 10 can select thecorresponding candidate neural network model, with the latency closer tothe time interval corresponding to the trained neural network model thanto the time intervals corresponding to the others of the candidateneural network models. For example, when the latency is 100 ms, theprocessor 10 can select the corresponding candidate neural network modelthat was trained with the 100 ms time interval. Therefore, the processor10 can use the selected candidate neural network model as the trainedneural network model for predicting orientation data. Accordingly, it ispossible to apply a respective network model for different latency, sothat the predicted orientation data can fit more accurately to theapplication in operation.

The present disclosure further discloses the experiment results ofpredicting the head movement using the orientation predicting method ofthe present disclosure and using the extrapolation method.

The experiment was carried out with 10 subjects. The 10 subjects wereasked to play first-person shooter (FPS) game using the virtual realityheadset, with the orientation data of each subject was collected duringthe game and used as the orientation training data. Since the FPS gameplayed in this experiment possesses a 50 ms latency, a data segmentcorresponding to a 50 ms latency is cut from each orientation trainingdata to obtain the adjusted orientation data. The orientation trainingdata and the adjusted orientation data from the 10 subjects were thenused to train an initial neural network model.

After training, the 10 subjects were asked to play the same FPS gameagain and a plurality of real-time orientation data of each subject wasalso collected during the game. That is, the plurality of real-timeorientation data was continuously collected for a duration, which is 5minutes in this experiment. For the convenience of description, theplurality of real-time orientation data of each subject herein includesa first real-time orientation data and a second real-time orientationdata, and the second real-time orientation data is the first real-timeorientation data 50 ms later.

Then the first real-time orientation data of the 10 subjects were bothinput to the trained neural network model and used in the extrapolationmethod respectively for predicting the head movement of the 10 subjects50 ms later. Namely, the first real-time orientation data is used toobtain the predicted orientation data by the trained neural networkmodel and the extrapolation method, and the predicted orientation dataobtained by both methods can then be compared with the second real-timedata respectively. After obtaining the predicted orientation data fromboth methods, the predicted orientation data and the second real-timedata are compared using inter-subject method. That is, errors betweenthe predicted orientation data obtained from the trained neural networkmodel and the second real-time orientation data with different movingspeeds and orientation indexes (roll, yaw and pitch), as well as errorsbetween the predicted orientation data obtained from the extrapolationand the second real-time orientation data with different moving speedsand orientation indexes are collected and compared.

Please refer to FIGS. 4A and 4B, wherein FIGS. 4A and 4B are statisticcharts showing differences between the predicted orientation data andthe second real-time orientation data based on a 50 ms latency, whereinthe differences are errors from using a linear extrapolation method andusing the orientation predicting method of the present disclosurerespectively, wherein the linear extrapolation method is described inparticular in the publication by Garcia-Agundez, A et al. entitled “Anevaluation of extrapolation and filtering techniques in head trackingfor virtual environments to reduce cybersickness.”, Joint InternationalConference on Serious Games (pp. 203-211). Springer, Cham.; Choi, S. Wet al. entitled “Prediction-based latency compensation technique forhead mounted display.”, in 2016 International SoC Desing Conference(ISOCC) (pp. 9-10), IEEE.; and LaValle, S. M. et al. entitled “Headtracking for the Oculus Rift.”, in 2014 IEEE International Conference onRobotics and Automation (ICRA) (pp. 187-194). IEEE.

For the simplification of the charts, the orientation predicting methodof the present disclosure shown in FIGS. 4A and 4B are represented as“AI”.

The errors shown in FIG. 4A include pitch error, roll error, and yawerror at different speeds. And the graphs showing the errors from usingthe orientation predicting method of the present disclosure (AI only)are shown in solid lines, the graphs showing the errors from usingextrapolation method are shown in dashed lines.

Pleaser first refer to FIG. 4A, the bar charts represent the errorscalculated using mean absolute error (MAE) function, and the curvesrepresent the maximal errors.

As seen from FIG. 4A, the MAE values of the extrapolation method areobviously higher than that of the AI only method whether the firstreal-time orientation data were obtained when the subject was moving ata normal speed or at a faster speed. Similarly, the maximal errors ofthe extrapolation method are obviously higher than that of the AI onlymethod.

Please refer to FIG. 4B, the bar charts represent correlationcoefficients between the predicted orientation data and the secondreal-time orientation data. As seen from FIG. 4B, the correlationcoefficients of the AI only method are higher than that of theextrapolation method whether at a normal speed or at a faster speed.

Please refer to FIGS. 5A and 5B, wherein FIGS. 5A and 5B are statisticcharts showing differences between the predicted orientation data andthe real-time orientation data based on a 100 ms latency, wherein thedifferences are errors from using the extrapolation method and using theorientation predicting method of the present disclosure respectively.Similar to FIGS. 4A and 4B, the orientation predicting method of thepresent disclosure shown here are represented as “AI”.

That is, the experiment of FIGS. 5A and 5B is similar to that of FIGS.4A and 4B, the difference between FIGS. 5A and 5B and FIGS. 4A and 4B isthat the latency of FIGS. 5A and 5B is 100 ms.

Similar to the result of FIG. 4A, the MAE values as well as the maximalerrors of the extrapolation method are obviously higher than that of theAI only method according to the bar chart in FIG. 5A. Further, thecorrelation coefficients of the AI only method are higher than that ofthe extrapolation method according to FIG. 5B.

In short, as seen from FIGS. 4A and 4B and FIGS. 5A and 5B, the errorscaused by the orientation predicting method of the present disclosureare significantly lower than using the extrapolation method (with pvalue<0.05); and the correlation between the predicted data and the realdata (the second real-time orientation data) are significantly higher inthe orientation predicting method of the present disclosure than in theextrapolation method (with p value<0.05). Therefore, it is obvious thatregardless of the moving speed and the duration of the latency, theorientation predicting method of the present disclosure is able topredict the user's head movement more accurately, thereby avoid motionsickness caused by the MTP latency when using virtual reality headset.

In view of the above description, according to one or more embodimentsof the orientation predicting method, a virtual reality headset and anon-transitory computer-readable medium of the present disclosure, theMTP latency can be effectively reduced and an accurate head movement ofthe user can be made, therefore, the user can have a better experiencewhen using the virtual reality headset without having motion sickness.

The present disclosure has been disclosed above in the embodimentsdescribed above, however it is not intended to limit the presentdisclosure. It is within the scope of the present disclosure to bemodified without deviating from the essence and scope of it. It isintended that the scope of the present disclosure is defined by thefollowing claims and their equivalents.

1. An orientation predicting method, adapted to a virtual realityheadset, comprising: obtaining an orientation training data and anadjusted orientation data, wherein the adjusted orientation data isobtained by cutting a data segment off from the orientation trainingdata, wherein the data segment corresponds to a time interval determinedby an application latency; training an initial neural network modelbased on the orientation training data and the adjusted orientation datafor obtaining a trained neural network model corresponding to the timeinterval; retrieving a real-time orientation data by an orientationsensor of the virtual reality headset; and inputting the real-timeorientation data to the trained neural network model to output apredicted orientation data.
 2. The method according to claim 1, whereinthe trained neural network model is one of a plurality of candidateneural network models, with said candidate neural network modelscorresponding to different time intervals respectively, and whereinbefore retrieving the real-time orientation data by the orientationsensor of the virtual reality headset, the method further comprises:estimating a latency of an application run by the virtual realityheadset; and selecting the trained neural network model from theplurality of candidate neural network models according to the latency,with the latency closer to the time interval corresponding to thetrained neural network model than to the time intervals corresponding tothe others of the candidate neural network models.
 3. The methodaccording to claim 1, wherein the initial neural network model comprisesa one-dimensional convolutional neural network.
 4. A virtual realityheadset, comprising: an orientation sensor, retrieving a real-timeorientation data of the virtual reality headset; a processor, inputtingthe real-time orientation data to a trained neural network model of theprocessor for obtaining a predicted orientation data, wherein thetrained neural network model is obtained by training an initial neuralnetwork model based on an orientation training data and an adjustedorientation data, wherein the adjusted orientation data is obtained bycutting a data segment off from the orientation training data, whereinthe data segment corresponds to a time interval determined by anapplication latency; and a screen, displaying a predicted imageaccording to the predicted orientation data.
 5. The virtual realityheadset according to claim 4, further comprising a memory storing aplurality of candidate neural network models including the trainedneural network model, wherein before inputting the real-time orientationdata to the trained neural network model, the processor furtherestimates a latency of an application run by the virtual realityheadset, and selects the trained neural network model from the pluralityof candidate neural network models, with the latency closer to the timeinterval corresponding to the trained neural network model than to thetime intervals corresponding to the others of the candidate neuralnetwork models.
 6. The virtual reality headset according to claim 4,wherein the initial neural network model comprises a one-dimensionalconvolutional neural network.
 7. A non-transitory computer-readablemedium, storing an executable instruction which, when executed, causes avirtual reality headset to perform a method comprising: retrieving areal-time orientation data by an orientation sensor and inputting thereal-time orientation data to a trained neural network model to output apredicted orientation data, wherein the trained neural network model isobtained by training an initial neural network model based on anorientation training data and an adjusted orientation data, wherein theadjusted orientation data is obtained by cutting a data segment off fromthe orientation training data, wherein the data segment corresponds to atime interval determined by an application latency.
 8. Thenon-transitory computer-readable medium according to claim 7, whereinthe trained neural network model is one of a plurality of candidateneural network models, with said candidate neural network modelscorresponding to different time intervals respectively, and whereinbefore receiving the real-time orientation data from the orientationsensor, further comprises: estimating a latency of an application run bythe virtual reality headset; and selecting the trained neural networkmodel from the plurality of candidate neural network models according tothe latency, with the latency closer to the time interval correspondingto the trained neural network model than to the time intervalscorresponding to the others of the candidate neural network models. 9.The non-transitory computer-readable medium according to claim 7,wherein the neural network model comprises a one-dimensionalconvolutional neural network.